The general aim of this proposal is to develop and verify micro-array based genomic mapping tools. These tools are based on representational approaches to genomic sampling, coupled with powerful statistical and algorithmic methods for data analysis. The tools are designed to enable integration of genetic, physical and transcript maps. Specifically, we describe: 1. Correspondence Mapping. This method makes assignments of arrayed probes to BACs that contain them, on a very large and parallel scale. It uses algorithms based on binary partitions. Among its uses are finding BACs corresponding to unfinished regions in genome assembly projects, finding BACs that correspond to expressed regions of the genome, and relating probes with annotation (for example, relating probes that have been physically or genetically mapped to probes that correspond to expressed sequences by finding BACs to which both probe sets belong.) 2. Linear Mapping. This method clusters probes into contigs, and within contigs establishes a linear ordering with pairwise probe distances. It uses algorithms based on Hamming metrics. It can be applied to fine mapping of the genome using a set of probes that are dense within the genome and a library of BACs, or to coarse mapping of a sparse set of probes and radiation hybrids. By using overlapping sets of probes, the coarse map can be used to orient the contigs from fine mapping. 3. Genetic Mapping. This method allows massive parallel array-based genotyping for a set of probes, VLCR SNPs, that derive from a "sliver" of the genome that can be amplified en masse. These probes are a subset of the probes that can be mapped by the other methods.